Zhezhuang Xu
Papers
2
Total Citations
7
H-Index
1
About
Zhezhuang Xu is a researcher whose work bridges intelligent systems, wireless sensor networks, and robotic automation. His research focuses on two interconnected domains: energy-efficient wireless charging for industrial sensor networks and the application of machine learning to robotic manipulation. In his notable 2020 work on wireless rechargeable sensor networks, Xu tackled a fundamental challenge in industrial IoT — coil misalignment during wireless charging — by integrating machine vision and communication technologies to optimize mobile charger control and significantly improve charging efficiency. This contribution addresses a critical bottleneck in prolonging sensor network lifetimes, earning 6 citations within the field. More recently, Xu has directed his attention toward advancing robotic grasping capabilities through offline reinforcement learning. His 2025 work on an improved QT-Opt algorithm targets persistent challenges in traditional online reinforcement learning, including distribution shift and action-space local optima, offering more robust and adaptive grasping strategies for robotic arms. Together, these contributions reflect Xu's broader vision of building smarter, more autonomous industrial systems — from self-sustaining sensor networks to intelligent robotic agents. His research is particularly relevant for students and practitioners working at the intersection of industrial automation, embedded systems, and applied machine learning.
Research Focus
Key Achievements
Top Papers
- 1
- 2